Life science researchers face an exponential expansion in data volumes, while wrestling with objectives including: minimising time-to-market, maximising efficacy at the same time as ensuring safety, and creating better-targeted therapies. Machine learning can turn this data from a time-sink into a resource that accelerates innovation. It can generate new ideas and provide insights that focus experimental programs and improve processes. In this paper, we discuss how machine learning addresses the data analysis challenge, drawing on examples of the use of our Alchemite™ machine learning software.
Example areas discussed are:
- Drug discovery
- Translational medicine
- Clinical study feasibility and product design
- Manufacturing and formulation
- Design of experiments